Software applications and systems have become indispensable tools for helping consumers, i.e., users, perform a wide variety of tasks in their daily professional and personal lives. Currently, numerous types of desktop, web-based, and cloud-based software systems are available to help users perform a plethora of tasks ranging from basic computing system operations and word processing, to financial management, small business management, tax preparation, health tracking and healthcare management, as well as other personal and business endeavors, operations, and functions far too numerous to individually delineate here.
One major, if not determinative, factor in the utility, and ultimate commercial success, of a given software system of any type is the ability to implement and provide a customer support system through which a given user can obtain assistance and, in particular, get answers to questions that arise during the installation and operation of the software system. However, providing potentially millions of software system users specialized advice and answers to their specific questions is a huge undertaking that can easily, and rapidly, become economically infeasible.
To address this problem, many providers of software systems implement or sponsor one or more question and answer based customer support systems. Typically, a question and answer based customer support system includes a hosted forum through which an asking user can direct their specific questions, typically in a text format, to a support community that often includes other users and/or professional agent support personnel.
In many cases, once an asking user's specific question is answered by members of the support community through the question and answer based customer support system, the asking user's specific question, and the answer to the specific question provided by the support community, is categorized and added to a customer support question and answer database associated with the question and answer based customer support system. In this way, subsequent searching users, i.e., a user accessing previously generated question and answer pairs, of the software system can access the asking users' specific questions or topics, and find the answer to the asking users' questions, via a search of the customer support question and answer database. As a result, a dynamic customer support question and answer database of categorized/indexed asking users questions and answers is made available to searching users of the software system through the question and answer based customer support system.
The development of customer support question and answer databases has numerous advantages including a self-help element whereby a searching user can find an answer to their particular question by simply searching the customer support question and answer database for topics, questions, and answers related to their issue previously submitted by asking users. Consequently, using a question and answer based customer support system including a customer support question and answer database, potentially millions of user questions can be answered in an efficient and effective manner, and with minimal duplicative effort.
Using currently available question and answer based customer support systems, once an asking user's question is answered, the asking user is provided the opportunity to rate the answer with respect to how helpful the answer was to the asking user. In addition, searching users in the user community are provided the opportunity to rate accessed question and answer pair content based on how helpful the answer was to them. In this way, feedback is provided with respect to a given question and answer pair, and answers with low satisfaction ratings, i.e., poorly rated answers, can eventually be identified by this feedback. In addition, this feedback data is also often used to determine/rank which question and answer pair, or pairs, to provide a searching user in response to question content submitted by the searching user.
Using traditional customer support question and answer databases, when a searching user submits a question, e.g., submits question data, to the customer support question and answer database, the customer support question and answer database is searched to determine if the question currently being asked has been answered before. Typically, if a determination is made that the question currently being asked, or a sufficiently similar question, has been answered before, the searching user is then provided one or more answers previously provided to the previously submitted questions determined to be the same as, or sufficiently similar to, the question currently being asked. Typically the searching user is then provided results data representing one or more previously asked question and answer pairs.
Using traditional customer support question and answer databases, the determination as to which previously answered question and answer pair, or pairs, are most likely to result in the searching user being satisfied with the answer provided is made largely, if not entirely, based on the feedback data, or ranking data, associated with the previously answered question and answer pair data provided by the original asking user and/or subsequent searching users as discussed above. As a result, using current question and answer based customer support systems, and their associated customer support question and answer databases, poorly rated, or low quality/value question and answer pair data is only removed reactively, after it has potentially been viewed by multiple users, and often a large number of searching users, if there is a significant delay between generating answer data and obtaining feedback regarding the question and answer pair data, especially from the original asking user. Consequently, using traditional customer support question and answer databases, it is imperative that feedback data, or ranking data, be obtained as quickly as possible, especially from the original asking user.
In addition, the determination as to which previously answered question and answer pair, or pairs, are most likely to result in the searching user being satisfied with the answer can only be made reactively after feedback data, or ranking data, associated with the previously answered question and answer pair data is provided by the original asking user and/or subsequent searching users. This is particularly problematic because until feedback data, or ranking data, regarding previously answered question and answer pair data is received from a significant number of users, the determination as to which previously answered question and answer pair, or pairs, are most likely to result in the searching user being satisfied with the answer can only be made on, at best, likely skewed and inaccurate data. Consequently, using traditional customer support question and answer databases, it is again imperative that feedback data, or ranking data, be obtained as quickly as possible, especially from the original asking user.
A related problem for providers of question and answer based customer support systems is the situation where an asking user submits question data to the question and answer based customer support system representing the asking user's question and then never returns to the question and answer based customer support system to check for, and/or review, answers to the submitted question, or in any way further engage the question and answer based customer support system. This is a problematic situation not only because precious support resources are wasted on an asker user who never again engages the question and answer based customer support system, but also because the non-engaging asking user never provides any feedback to the question and answer based customer support system, the support community, or other users. Consequently, these non-engaging asking users are a significant drain on customer support resources associated with the question and answer based customer support system.
The above situation presents several challenges to the providers of question and answer based customer support systems, and their associated customer support question and answer databases. These challenges are partially significant given that a customer support question and answer database is usually a critical, if not the most important, feature of a question and answer based customer support system. This is because there is, by definition, a finite number of support resources, such as, for example, support personnel, either volunteers or professionals, and, therefore, there is a limit to the amount of support resources, such as support person-hours, available at any time to answer user questions. Consequently, it is important to utilize support resources, such as a support community, efficiently not only to answer questions in a reasonable timeframe, but to answer questions that are likely to result in engaged and satisfied asking users first, as opposed to questions submitted by non-engaging asking users who, once their question is submitted, never reengage with the question and answer based customer support system to even check for, and/or read, answers to their question, much less participate in the question and answer based customer support system by leaving feedback, review, or ratings data.
As noted above, to most efficiently utilize support resources, such as volunteer and professional agent support personnel of a support community, it is desirable to focus those support resources on questions submitted by asking users who are likely to engage the question and answer based customer support system after their questions are submitted, read the answer(s) to their submitted questions, and, ideally, rate and/or review the question answer data, and/or the question and answer based customer support system and support community. In this way, the use of the support resources will yield more positive results and a customer support question and answer database will be developed, and/or dynamically adapted, to provide higher quality answer content predicted to provide a greater number of users with answer content meeting their needs.
Despite this long standing need, traditional question and answer based customer support systems typically do not address the issue discussed above. This is largely because, using traditional question and answer based customer support systems, analysis of question and answer data is largely preformed reactively only after the answer data has been generated, and after the support resources, such as volunteer and professional agent support personnel of a support community, have been devoted to answering the question. Consequently, using traditional question and answer based customer support systems, precious support resources are often devoted to questions submitted by asking users who are not likely to engage the question and answer based customer support system after their questions are submitted, read the answer to their submitted questions, or rate and/or review the question answers and/or the question and answer based customer support system.
In addition, to make matters worse, it is often the case that much more precious support resources are wasted trying to answer questions submitted by asking users who are not likely to engage the question and answer based customer support system after their questions are submitted than those asking users who are likely to engage the question and answer based customer support system after their questions are submitted. This is because, unfortunately, questions submitted by asking users who are not likely to engage the question and answer based customer support system are often low quality questions in a low quality question format.
Clearly, the situation described above represents a significant issue and a long standing problem for question and answer based customer support systems and software system providers. This is because user satisfaction with the question and answer based customer support systems is not only critical to the effectiveness of the question and answer based customer support systems, but also to the satisfaction and reputation of the software system and the software system provider. As a result of the situation described above, currently, both users and providers of software systems, and question and answer based customer support systems of all types, are denied the full potential of the question and answer based customer support systems. Consequently, the technical fields of information dissemination, customer support, feedback utilization and integration, software implementation and operation, and user experience are detrimentally affected.
What is needed is a method and system for reliably, efficiently, and proactively predicting an asking user's post question submission engagement with a question and answer based customer support system before any significant support resources were devoted to answering the asking user's question. In this way, questions submitted by asking users determined to have a low asking user engagement probability, i.e., asking users that are not likely to engage the question and answer based customer support system after the question is submitted, could be given a low priority, or could even be ignored, while questions submitted by asking users determined to have a high asking user engagement probability, i.e., asking users that are likely to engage the question and answer based customer support system after the question is submitted, could be given a high priority. In this way, support resources, such as the time and energy of support personnel, could utilized most efficiently.